welcome!
Hello there,
I'm Charlie Piper, a software engineer intern based in London, United Kingdom.
I also study an Artifical Intelligence course at the University of York, trying to make projects on the side :)
works
march, 2024
gaia
web development
This was a website that I designed for a packaging company, their old website was a generic wordpress site that was not only poorly designed, but also cumbersome and slow, and not user-friendly.
The redesign involved translating existing components / content into brand new, efficient NextJS components, and using TailwindCSS to style the site. It was also crucial to not delete any existing content, as the company had a lot of SEO value in their old site. But also existing customers should be familiar with the layout of the new site.
december, 2023
fabric
ecommerce website
I designed a comprehensive ecommerce website to provide an optimal shopping experience. The site features a clean, modern design and is highly responsive to ensure seamless browsing on all devices.
Key functionalities include a robust product catalog, a smooth checkout process, and a Stripe payment system to enhance user convenience. The design prioritizes user experience and accessibility, making it easy for customers to navigate and find products quickly. The project involved using NextJS for its powerful performance benefits and TailwindCSS for streamlined styling. The result is a fast, visually appealing, and user-friendly ecommerce platform.
may, 2024
engineering module
university module
This project involved designing a website to showcase our team's work for a university engineering module. The goal was to present our project in a way that other teams could review all our work, potentially selecting it to continue working on. Our website successfully garnered interest and resulted in different teams choosing our project to take over and develop further.
may, 2024
convolutional neural network paper
university module
A paper I wrote on convolutional neural networks for a university module. The purpose of this project was to classify flower species over a large number of classes. This involved designing a deep convolutional neural network from scratch and training it on the Oxford 102 dataset, using a large computing server, Viking. After hundreds of iterations, the model achieved an overall accruacy of 76.22% - which was severely limited by the dataset and the inherent limits of an untrained model, only being allowed to train for a maximum of 12 hours.
The paper was written in LaTeX, and follows the iEE guidelines for academic papers. And the code was written in Python, using the PyTorch library.
summer, 2024
I made this website to introduce a little bit more of myself online, if you would like my cv or just to talk, feel free :^)